Reporting outcome measures in veterinary physiotherapy with particular reference to the treatment of canine and equine joint cases in the UK

BACKGROUND

Outcome measures are extensively used within human physiotherapy, but a widely accepted issue in veterinary physiotherapy is that outcome measures lack sufficient evaluation and standardisation in terms of how they are implemented. This cross-sectional study aimed to provide clarity on (1) the current selection of outcome measures in canine and equine physiotherapy and (2) investigate external influences on outcome measure selection, including comparative literature availability, professional memberships and background.

METHODS

A structured scoping literature review consolidated current understanding and limitations. This informed a survey of qualified veterinary physiotherapists (n = 40). The statistical analysis comprised descriptive statistics.

RESULTS

Key observations included (1) a lack of difference in outcome measure application between veterinary physiotherapists with and without a human physiotherapy background, (2) enhanced outcome measure utilisation by registry body members and (3) an overall skew towards subjective, rather than objective, outcome measure use.

LIMITATIONS

The study was limited by the absence of a defined veterinary physiotherapist population and subsequent convenience sample size.

CONCLUSION

The apparent skew towards subjective outcome measures highlights objective outcome measure underutilisation and the need for a more extensive evidence base. In conclusion, there is a need to develop comprehensive professional development resources promoting the use of repeatable outcome measures such as goniometers and the Liverpool osteoarthritis scoring.

Automated movement tracking of young autistic children during free play is correlated with clinical features associated with autism

LAY ABSTRACT

Play-based observations allow researchers to observe autistic children across a wide range of ages and skills. We recorded autistic children playing with toys in the center of a room and at a corner table while a caregiver remained seated off to the side and used video tracking technology to track children's movement and location. We examined how time children spent in room regions and whether or not they approached each region during play related to their cognitive, social, communication, and adaptive skills to determine if tracking child movement and location can meaningfully demonstrate clinical variation among autistic children representing a range of ages and skills. One significant finding was that autistic children who spent more time in the toy-containing center of the room had higher cognitive and language abilities, whereas those who spent less time in the center had higher levels of autism-related behaviors. In contrast, children who spent more time in the caregiver region had lower daily living skills and those who were quicker to approach the caregiver had lower adaptive behavior and language skills. These findings support the use of movement tracking as a complementary method of measuring clinical differences among autistic children. Furthermore, over 90% of autistic children representing a range of ages and skills in this study provided analyzable play observation data, demonstrating that this method allows autistic children of all levels of support needs to participate in research and demonstrate their social, communication, and attention skills without wearing any devices.

Detection of Pig Movement and Aggression Using Deep Learning Approaches

Motion and aggressive behaviors in pigs provide important information for the study of social hierarchies in pigs and can be used as a selection indicator for pig health and aggression parameters. However, relying only on visual observation or surveillance video to record the number of aggressive acts is time-consuming, labor-intensive, and lasts for only a short period of time. Manual observation is too short compared to the growth cycle of pigs, and complete recording is impractical in large farms. In addition, due to the complex process of assessing the intensity of pig aggression, manual recording is highly influenced by human subjective vision. In order to efficiently record pig motion and aggressive behaviors as parameters for breeding selection and behavioral studies, the videos and pictures were collected from typical commercial farms, with each unit including 8~20 pigs in 7~25 m2 space; they were bred in stable social groups and a video was set up to record the whole day's activities. We proposed a deep learning-based recognition method for detecting and recognizing the movement and aggressive behaviors of pigs by recording and annotating head-to-head tapping, head-to-body tapping, neck biting, body biting, and ear biting during fighting. The method uses an improved EMA-YOLOv8 model and a target tracking algorithm to assign a unique digital identity code to each pig, while efficiently recognizing and recording pig motion and aggressive behaviors and tracking them, thus providing statistics on the speed and duration of pig motion. On the test dataset, the average precision of the model was 96.4%, indicating that the model has high accuracy in detecting a pig's identity and its fighting behaviors. The model detection results were highly correlated with the manual recording results (R2 of 0.9804 and 0.9856, respectively), indicating that the method has high accuracy and effectiveness. In summary, the method realized the detection and identification of motion duration and aggressive behavior of pigs under natural conditions, and provided reliable data and technical support for the study of the social hierarchy of pigs and the selection of pig health and aggression phenotypes.

Captive Animal Behavior Study by Video Analysis

Three video analysis-based applications for the study of captive animal behavior are presented. The aim of the first one is to provide certain parameters to assess drug efficiency by analyzing the movement of a rat. The scene is a three-chamber plastic box. First, the rat can move only in the middle room. The rat's head pose is the first parameter needed. Secondly, the rodent could walk in all three compartments. The entry number in each area and visit duration are the other indicators used in the final evaluation. The second application is related to a neuroscience experiment. Besides the electroencephalographic (EEG) signals yielded by a radio frequency link from a headset mounted on a monkey, the head placement is a useful source of information for reliable analysis, as well as its orientation. Finally, a fusion method to construct the displacement of a panda bear in a cage and the corresponding motion analysis to recognize its stress states are shown. The arena is a zoological garden that imitates the native environment of a panda bear. This surrounding is monitored by means of four video cameras. We have applied the following stages: (a) panda detection for every video camera; (b) panda path construction from all routes; and (c) panda way filtering and analysis.

Immediate and delayed movement of resistant and susceptible adults of Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae) after short exposures to phosphine

BACKGROUND

During the last decade, the evaluation of certain behavioral attributes has been utilized as an indicator of resistance to phosphine. In this context, an underappreciated challenge may be the development of behavioral traits that are related with resistance to phosphine such as the movement to refugia and recovery of stored product insects after short exposures. Thus, the aim of the current study was to track the movement of phosphine-resistant and -susceptible adults of the red flour beetle, Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae), which is a major pest of stored products, after brief exposures to phosphine. Exposures were followed for extended intervals to assess the recovery patterns and how those patterns are related to known resistance to phosphine. A video-tracking procedure coupled with Ethovision software was used to assess movement after exposure.

RESULTS

Overall, we found baseline movement was less for phosphine-resistant T. castaneum, suggesting resistance comes at a considerable fitness cost. In the presence of phosphine (1000 or 3000 ppm), there was a much greater reduction in movement for phosphine-susceptible than phosphine-resistant T. castaneum adults immediately after brief 5-min exposures. Twenty-four hours later, these effects were more variable and less apparent, regardless of the susceptibility level.

CONCLUSIONS

The initial knockdown associated with successful fumigation may just be a temporary state whereafter insects shortly resume movement and may be able to seek out refugia from phosphine, thereby promoting the development of resistance. Our results strengthen a growing consensus that it is the speed to knockdown that truly matters, with quick knockdown indicating slow recovery and a reduced likelihood for the occurrence of resistance. © 2023 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Seedling maize counting method in complex backgrounds based on YOLOV5 and Kalman filter tracking algorithm

Maize population density is one of the most essential factors in agricultural production systems and has a significant impact on maize yield and quality. Therefore, it is essential to estimate maize population density timely and accurately. In order to address the problems of the low efficiency of the manual counting method and the stability problem of traditional image processing methods in the field complex background environment, a deep-learning-based method for counting maize plants was proposed. Image datasets of the maize field were collected by a low-altitude UAV with a camera onboard firstly. Then a real-time detection model of maize plants was trained based on the object detection model YOLOV5. Finally, the tracking and counting method of maize plants was realized through Hungarian matching and Kalman filtering algorithms. The detection model developed in this study had an average precision mAP@0.5 of 90.66% on the test dataset, demonstrating the effectiveness of the SE-YOLOV5m model for maize plant detection. Application of the model to maize plant count trials showed that maize plant count results from test videos collected at multiple locations were highly correlated with manual count results (R² = 0.92), illustrating the accuracy and validity of the counting method. Therefore, the maize plant identification and counting method proposed in this study can better achieve the detection and counting of maize plants in complex backgrounds and provides a research basis and theoretical basis for the rapid acquisition of maize plant population density.

Assessing the effect of complex ground types on ground-dwelling arthropod movements with video monitoring: Dealing with concealed movements under a layer of plant residues

Understanding the effect of ground types on foraging movements of ground-dwelling arthropods is a key step to managing their spatial distribution as required for successful conservation biological control. Indeed, fine movements at the centimeter scale can strongly influence the foraging ability of pest predators. However, because radio frequency identification or harmonic tracking techniques are not yet suitable for small species and video tracking focuses on uniform and light backgrounds, foraging movements have rarely been studied in relation to ground types. We present a method to track a ground-dwelling arthropod (the earwig Euborellia caraibea) at night, walking on two contrasted ground types: bare soil and soil partly covered with a stratum of banana plant residues allowing individuals to hide periodically. The tracking of individuals within these ground types was achieved by infrared light, tagging individuals, video treatments, and semi-automatic cleaning of trajectories. We tested different procedures to obtain segments with identical durations to quantify speeds and sinuosities. These procedures were characterized by the junction time gap between trajectory fragments, the rediscretization time of trajectories, and whether or not to use interpolation to fill in missing points in the trajectories. Earwigs exhibited significantly slower and more sinuous movements on soil with banana plant residues than on bare soil. Long time gaps for trajectory junction, extended rediscretization times, and interpolation were complementary means to integrate concealed movements in the trajectories. The highest slowdown in plant residues was detected when the procedure could account for longer periods under the residues. These results suggest that earwigs spent a significant amount of time concealed by the residues. Additionally, the residues strongly decreased the earwigs' movement. Since the technical solutions presented in this study are inexpensive, easy to set up, and replicate, they represent valuable contributions to the emerging field of video monitoring.

Design of a Low-Power Embedded System Based on a SoC-FPGA and the Honeybee Search Algorithm for Real-Time Video Tracking

Video tracking involves detecting previously designated objects of interest within a sequence of image frames. It can be applied in robotics, unmanned vehicles, and automation, among other fields of interest. Video tracking is still regarded as an open problem due to a number of obstacles that still need to be overcome, including the need for high precision and real-time results, as well as portability and low-power demands. This work presents the design, implementation and assessment of a low-power embedded system based on an SoC-FPGA platform and the honeybee search algorithm (HSA) for real-time video tracking. HSA is a meta-heuristic that combines evolutionary computing and swarm intelligence techniques. Our findings demonstrated that the combination of SoC-FPGA and HSA reduced the consumption of computational resources, allowing real-time multiprocessing without a reduction in precision, and with the advantage of lower power consumption, which enabled portability. A starker difference was observed when measuring the power consumption. The proposed SoC-FPGA system consumed about 5 Watts, whereas the CPU-GPU system required more than 200 Watts. A general recommendation obtained from this research is to use SoC-FPGA over CPU-GPU to work with meta-heuristics in computer vision applications when an embedded solution is required.

Quantitative Gait Analysis Using a Pose-Estimation Algorithm with a Single 2D-Video of Parkinson's Disease Patients

BACKGROUND

Clinician-based rating scales or questionnaires for gait in Parkinson's disease (PD) are subjective and sensor-based analysis is limited in accessibility.

OBJECTIVE

To develop an easily accessible and objective tool to evaluate gait in PD patients, we analyzed gait from a single 2-dimensional (2D) video.

METHODS

We prospectively recorded 2D videos of PD patients (n = 16) and healthy controls (n = 15) performing the timed up and go test (TUG). The gait was simultaneously evaluated with a pressure-sensor (GAITRite). We estimated the 3D position of toes and heels with a deep-learning based pose-estimation algorithm and calculated gait parameters including step length, step length variability, gait velocity and step cadence which was validated with the result from the GAITRite. We further calculated the time and steps required for turning. Then, we applied the algorithm to previously recorded and archived videos of PD patients (n = 32) performing the TUG.

RESULTS

From the validation experiment, gait parameters derived from video tracking were in excellent agreement with the parameters obtained with the GAITRite. (Intraclass correlation coefficient > 0.9). From the analysis with the archived videos, step length, gait velocity, number of steps, and the time required for turning were significantly correlated (Absolute R > 0.4, p < 0.005) with the Freezing of gait questionnaire, Unified PD Rating scale part III total score, HY stage and postural instability. Furthermore, the video-based tracking objectively measured significant improvement of step length, gait velocity, steps and the time required for turning with antiparkinsonian medication.

CONCLUSION

2D video-based tracking could objectively evaluate gait in PD patients.

Behavioral Analysis and Individual Tracking Based on Kalman Filter: Application in an Urban Environment

In order to improve behavioral analysis systems in urban environments, this paper proposes, using data extracted from video surveillance cameras, a tracking method through two approaches. The first approach consists in comparing the position of people between two images of a video and to perform tracking by proximity. The second method using Kalman filters is based on the anticipation of the position of an individual in the upcoming image. The use of this method proves to be more efficient as it allows continuing a detection even when people cross each other or when they pass behind obstacles. The use of Kalman filters in this domain provides a new approach to obtain reliable tracking and information on speed and trajectory variations. The proposed method is innovative in the way the tracking is performed and the results are exploited. Experiments were conducted in a real situation and showed that the use of some elements of the first method could be reused to integrate a notion of distance in the method based on the Kalman filter and thus improve the latter both in tracking and in detecting of abnormal behavior. This article deals with the functioning of the two methods as well as the results obtained with the same scenarios. The experimentation concludes through concrete results that the Kalman filter method is more efficient than the proximity method alone. A sample result is available online for two of the seven videos used in this article (accessed on 19 July 2021).

Regions of interest selection and thermal imaging data analysis in sports and exercise science: a narrative review

OBJECTIVE

Infrared thermography (IRT) is a non-invasive, contactless and low-cost technology that allows recording of the radiating energy that is released from a body, providing an estimate of its superficial temperature. Thanks to the improvement of infrared thermal detectors, this technique is widely used in the biomedical field to monitor the skin temperature for different purposes (e.g. assessing circulatory diseases, psychophysiological state, affective computing). Particularly, in sports and exercise science, thermography is extensively used to assess sports performance, to investigate superficial vascular changes induced by physical exercise, and to monitor injuries. However, the methods of analysis employed to treat IRT data are not standardized, and hence introduce variability in the results.

APPROACH

This review focuses on the methods of analysis currently used for thermal imaging in sports and exercise science.

MAIN RESULTS

Firstly, the procedures employed for the selection of regions of interest (ROIs) from anatomical body districts are reviewed, paying attention also to the potentialities of morphing algorithms to increase the reproducibility of thermal results. Secondly, the statistical approaches utilized to characterize the temperature frequency and spatial distributions within ROIs are investigated, showing their strengths and weaknesses. Moreover, the importance of employing tracking methods to analyze the temporal thermal oscillations within ROIs is discussed. Thirdly, the capability of employing procedures of investigation based on machine learning frameworks on thermal imaging in sports science is examined.

SIGNIFICANCE

Finally, some proposals to improve the standardization and the reproducibility of IRT data analysis are provided, in order to facilitate the development of a common database of thermal images and to improve the effectiveness of IRT in sports science.

Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash

In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences. Sport science in general, as well as coaches and athletes in particular, would greatly benefit from HPE-CNN-based tracking, but the sheer amount of HPE-CNNs available, as well as their complexity, pose a hurdle to the adoption of this new technology. It is unclear how many HPE-CNNs which are available at present are ready to use in out-of-the-box inference to squash, to what extent they allow motion analysis and if detections can easily be used to provide insight to coaches and athletes. Therefore, we conducted a systematic investigation of more than 250 HPE-CNNs. After applying our selection criteria of open-source, pre-trained, state-of-the-art and ready-to-use, five variants of three HPE-CNNs remained, and were evaluated in the context of motion analysis for the racket sport of squash. Specifically, we are interested in detecting player's feet in videos from a single camera and investigated the detection accuracy of all HPE-CNNs. To that end, we created a ground-truth dataset from publicly available squash videos by developing our own annotation tool and manually labeling frames and events. We present heatmaps, which depict the court floor using a color scale and highlight areas according to the relative time for which a player occupied that location during matchplay. These are used to provide insight into detections. Finally, we created a decision flow chart to help sport scientists, coaches and athletes to decide which HPE-CNN is best for player detection and tracking in a given application scenario.

Rest Is Required to Learn an Appetitively-Reinforced Operant Task in Drosophila

Maladaptive operant conditioning contributes to development of neuropsychiatric disorders. Candidate genes have been identified that contribute to this maladaptive plasticity, but the neural basis of operant conditioning in genetic model organisms remains poorly understood. The fruit fly Drosophila melanogaster is a versatile genetic model organism that readily forms operant associations with punishment stimuli. However, operant conditioning with a food reward has not been demonstrated in flies, limiting the types of neural circuits that can be studied. Here we present the first sucrose-reinforced operant conditioning paradigm for flies. In the paradigm, flies walk along a Y-shaped track with reward locations at the terminus of each hallway. When flies turn in the reinforced direction at the center of the track, they receive a sucrose reward at the end of the hallway. Only flies that rest early in training learn the reward contingency normally. Flies rewarded independently of their behavior do not form a learned association but have the same amount of rest as trained flies, showing that rest is not driven by learning. Optogenetically-induced sleep does not promote learning, indicating that sleep itself is not sufficient for learning the operant task. We validated the sensitivity of this assay to detect the effect of genetic manipulations by testing the classic learning mutant dunce. Dunce flies are learning-impaired in the Y-Track task, indicating a likely role for cAMP in the operant coincidence detector. This novel training paradigm will provide valuable insight into the molecular mechanisms of disease and the link between sleep and learning.

In vivo assay and modelling of protein and mitochondrial turnover during aging

To maintain homoeostasis, cells must degrade damaged or misfolded proteins and synthesize functional replacements. Maintaining a balance between these processes, known as protein turnover, is necessary for stress response and cellular adaptation to a changing environment. Damaged mitochondria must also be removed and replaced. Changes in protein and mitochondrial turnover are associated with aging and neurodegenerative disease, making it important to understand how these processes occur and are regulated in cells. To achieve this, reliable assays of turnover must be developed. Several methods exist, including pulse-labelling with radioactive or stable isotopes and strategies making use of fluorescent proteins, each with their own advantages and limitations. Both cell culture and live animals have been used for these studies, in systems ranging from yeast to mammals. In vivo assays are especially useful for connecting turnover to aging and disease. With its short life cycle, suitability for fluorescent imaging, and availability of genetic tools, Drosophila melanogaster is particularly well suited for this kind of analysis.

Integrating real-time data analysis into automatic tracking of social insects

Automatic video tracking has become a standard tool for investigating the social behaviour of insects. The recent integration of computer vision in tracking technologies will probably lead to fully automated behavioural pattern classification within the next few years. However, many current systems rely on offline data analysis and use computationally expensive techniques to track pre-recorded videos. To address this gap, we developed BACH (Behaviour Analysis maCHine), a software that performs video tracking of insect groups in real time. BACH uses object recognition via convolutional neural networks and identifies individually tagged insects via an existing matrix code recognition algorithm. We compared the tracking performances of BACH and a human observer (HO) across a series of short videos of ants moving in a two-dimensional arena. We found that BACH detected ant shapes only slightly worse than the HO. However, its matrix code-mediated identification of individual ants only attained human-comparable levels when ants moved relatively slowly, and fell when ants walked relatively fast. This happened because BACH had a relatively low efficiency in detecting matrix codes in blurry images of ants walking at high speeds. BACH needs to undergo hardware and software adjustments to overcome its present limits. Nevertheless, our study emphasizes the possibility of, and the need for, further integrating real-time data analysis into the study of animal behaviour. This will accelerate data generation, visualization and sharing, opening possibilities for conducting fully remote collaborative experiments.

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting.

Carotenoids in the eyespot apparatus are required for triggering phototaxis in Euglena gracilis

Carotenoids are the most universal and most widespread pigments in nature. They have played pivotal roles in the evolution of photosensing mechanisms in microbes and of vision in animals. Several groups of phytoflagellates developed a photoreceptive organelle called the eyespot apparatus (EA) consisting of two separable components: the eyespot, a cluster of carotenoid-rich globules that acts as a reflector device, and actual photoreceptors for photobehaviors. Unlike other algal eyespots, the eyespot of Euglenophyta lacks reflective properties and is generally considered to act as a shading device for the photoreceptor (paraflagellar body, PFB) for major photomovements. However, the function of the eyespot of Euglenophyta has not yet been fully proven. Here, we report that the blocking carotenoid biosynthesis in Euglena gracilis by suppressing the phytoene synthase gene (crtB) caused a defect in eyespot function resulting in a loss of phototaxis. Raman spectroscopy and transmission electron microscopy suggested that EgcrtB-suppressed cells formed eyespot globules but had a defect in the accumulation of carotenoids in those packets. Motion analysis revealed the loss of phototaxis in EgcrtB-suppressed cells: a defect in the initiation of turning movements immediately after a change in light direction, rather than a defect in the termination of cell turning at the appropriate position due to a loss of the shading effect on the PFB. This study revealed that carotenoids are essential for light perception by the EA for the initiation of phototactic movement by E. gracilis, suggesting one possible photosensory role of carotenoids in the EA for the phototaxis.

Biomarker Acquisition and Quality Control for Multi-Site Studies: The Autism Biomarkers Consortium for Clinical Trials

The objective of the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is to evaluate a set of lab-based behavioral video tracking (VT), electroencephalography (EEG), and eye tracking (ET) measures for use in clinical trials with children with autism spectrum disorder (ASD). Within the larger organizational structure of the ABC-CT, the Data Acquisition and Analytic Core (DAAC) oversees the standardization of VT, EEG, and ET data acquisition, data processing, and data analysis. This includes designing and documenting data acquisition and analytic protocols and manuals; facilitating site training in acquisition; data acquisition quality control (QC); derivation and validation of dependent variables (DVs); and analytic deliverables including preparation of data for submission to the National Database for Autism Research (NDAR). To oversee consistent application of scientific standards and methodological rigor for data acquisition, processing, and analytics, we developed standard operating procedures that reflect the logistical needs of multi-site research, and the need for well-articulated, transparent processes that can be implemented in future clinical trials. This report details the methodology of the ABC-CT related to acquisition and QC in our Feasibility and Main Study phases. Based on our acquisition metrics from a preplanned interim analysis, we report high levels of acquisition success utilizing VT, EEG, and ET experiments in a relatively large sample of children with ASD and typical development (TD), with data acquired across multiple sites and use of a manualized training and acquisition protocol.

A pilot study on assessment of locomotor behavior using a video tracking system in minipigs

Pigs are often selected for large animal models including for neuroscience and behavioral research, because their anatomy and biochemistry are similar to those of humans. However, behavioral assessments, in combination with objective long-term monitoring, is difficult. In this study, we introduced an automated video tracking system which was previously used in rodent studies, for use with pig models. Locomotor behaviors (total distance, number of zone transitions, and velocity) were evaluated and their changes were validated by different 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) administration methods and dosing regimens. Three minipigs (23-29 kg) received subcutaneous or intravenous MPTP, either 1 or 3 times per week. Immediately after MPTP injection, the minipigs remained in a corner and exhibited reduced trajectory. In addition, the total distance travelled, number of zone transitions, and velocity were greatly reduced at every MPTP administration in all the minipigs, accompanying to increased resting time. However, the MPTP-induced symptoms were reversed when MPTP administration was terminated. In conclusion, this automated video-tracking system was able to monitor long-term locomotor activity and differentiate detailed alterations in large animals. It has the advantages of being easy to use, higher resolution, less effort, and more delicate tracking. Additionally, as our method can be applied to the animals' home pen, no habituation is needed.

An Integrated System for the Automated Recording and Analysis of Insect Behavior in T-maze Arrays

Host-plant resistance to insects like thrips and aphids is a complex trait that is difficult to phenotype quickly and reliably. Here, we introduce novel hardware and software to facilitate insect choice assays and automate the acquisition and analysis of movement tracks. The hardware consists of an array of individual T-mazes allowing simultaneous release of up to 90 insect individuals from their individual cage below each T-maze with choice of two leaf disks under a video camera. Insect movement tracks are acquired with computer vision software (EthoVision) and analyzed with EthoAnalysis, a novel software package that allows for automated reporting of highly detailed behavior parameters and statistical analysis. To validate the benefits of the system we contrasted two Arabidopsis accessions that were previously analyzed for differential resistance to western flower thrips. Results of two trials with 40 T-mazes are reported and we show how we arrived at optimized settings for the different filters and statistics. The statistics are reported in terms of frequency, duration, distance and speed of behavior events, both as sum totals and event averages, and both for the total trial period and in time bins of 1 h. Also included are higher level analyses with subcategories like short-medium-long events and slow-medium-fast events. The time bins showed how some behavior elements are more descriptive of differences between the genotypes during the first hours, whereas others are constant or become more relevant at the end of an 8 h recording. The three overarching behavior categories, i.e., choice, movement, and halting, were automatically corrected for the percentage of time thrips were detected and 24 out of 38 statistics of behavior parameters differed by a factor 2-6 between the accessions. The analysis resulted in much larger contrasts in behavior traits than reported previously. Compared to leaf damage assays on whole plants or detached leaves that take a week or more to complete, results were obtained in 8 h, with more detail, fewer individuals and higher significance. The potential value of the new integrated system, named EntoLab, for discovery of genetic traits in plants and insects by high throughput screening of large populations is discussed.

To eat or get heat: Behavioral trade-offs between thermoregulation and feeding in gregarious necrophagous larvae

The thermoregulation behavior of Lucilia sericata larvae (Diptera: Calliphoridae), a necrophagous species that feeds on vertebrate cadavers, was investigated. These larvae require high heat incomes to develop, and can elevate temperatures by forming large aggregates. We hypothesized that L. sericata larvae should continue to feed at temperatures up to 38 °C, which can be reached inside larval masses. Thermal regulation behavior such as movement between a hot food spot and colder areas was also postulated. The hypotheses were tested by tracking for 1 h the activity of single, starved third instar larvae in a Petri dish containing 1 food spot (FS) that was heated to a constant temperature of 25 °C, 34 °C or 38 °C with an ambient temperature of 25 °C. The influence of previous conspecific activity in the food on larval behavior was also tested. The crops of larvae were dissected to monitor food content in the digestive systems. Based on relative crop measurements, larvae fed at all food temperatures, but temperature strongly affected larval behavior and kinematics. The total time spent by larvae in FS and the duration of each stay decreased at high FS temperature. Previous activity of conspecifics in the food slightly increased the time spent by larvae in FS and also decreased the average distance to FS. Therefore, necrophagous L. sericata larvae likely thermoregulate during normal feeding activities by adjusting to local fluctuations in temperature, particularly inside maggot masses. By maintaining a steady internal body temperature, larvae likely reduce their development time.

High-Speed Videography Reveals How Honeybees Can Turn a Spatial Concept Learning Task Into a Simple Discrimination Task by Stereotyped Flight Movements and Sequential Inspection of Pattern Elements

Honey bees display remarkable visual learning abilities, providing insights regarding visual information processing in a miniature brain. It was discovered that bees can solve a task that is generally viewed as spatial concept learning in primates, specifically the concept of “above” and “below.” In these works, two pairs of visual stimuli were shown in the two arms of a Y-maze. Each arm displayed a “referent” shape (e.g., a cross, or a horizontal line) and a second geometric shape that appeared either above or below the referent. Bees learning the “concept of aboveness” had to choose the arm of the Y-maze in which a shape–any shape–occurred above the referent, while those learning the “concept of belowness” had to pick the arm in which there was an arbitrary item beneath the referent. Here, we explore the sequential decision-making process that allows bees to solve this task by analyzing their flight trajectories inside the Y-maze. Over 368 h of high-speed video footage of the bees' choice strategies were analyzed in detail. In our experiments, many bees failed the task, and, with the possible exception of a single forager, bees as a group failed to reach significance in picking the correct arm from the decision chamber of the maze. Of those bees that succeeded in choosing correctly, most required a close-up inspection of the targets. These bees typically employed a close-up scan of only the bottom part of the pattern before taking the decision of landing on a feeder. When rejecting incorrect feeders, they repeatedly scanned the pattern features, but were still, on average, faster at completing the task than the non-leaners. This shows that solving a concept learning task could actually be mediated by turning it into a more manageable discrimination task by some animals, although one individual in this study appeared to have gained the ability (by the end of the training) to solve the task in a manner predicted by concept learning.

A noninterfering system to measure in-cage spontaneous physical activity in mice

Due to lack of low-cost and convenient measurement procedures, uncontrolled changes in spontaneous physical activity (SPA) level often are insufficiently considered as a confounding factor in rodent studies. Nonetheless, alterations in SPA can significantly impact on a wide range of physiological measurements. Therefore, we developed an accurate, low-cost video tracking procedure to allow routine assessment of SPA in the home cage of experimental animals (i.e., mice) and in the absence of any distress that might cause alterations in SPA. SPA parameters acquired (movement distance, movement time, and movement speed) with the novel tracking system were identical to those simultaneously obtained with a high-end and well-validated movement-tracking device (mean error = 0.15 ± 0.07%, r = 0.99, P < 0.001). To further validate the setup, we also demonstrated caffeine-induced stimulation of SPA (195% more activity compared with vehicle, P < 0.01), we adequately reproduced typical SPA fluctuations inherent to day/night cycles (146 and 702% more active during nocturnal compared with diurnal cycle for Balb/c and C57BL/6J mice, respectively, P < 0.001), and we confirmed previously documented SPA differences between animal strains (24% less activity in C57BL/6J mice compared with Balb/c mice, P < 0.05). Taken together, we provide data to prove that this novel low-cost methodology can be conveniently used in any mouse experiment where uncontrolled changes in SPA due to experimental interventions might confound data interpretation. By analogy, the system can be used to document a beneficial impact of therapeutic interventions on SPA in any disease mouse model. NEW & NOTEWORTHY We developed a low-cost procedure to routinely measure SPA in mice. The procedure maintains normal SPA because the animals continue to stay in their home cage in the absence of any external manipulation by the investigators and under habitual dark/light ambient conditions. This novel methodology can be conveniently used in any mouse experiment to quantify experimentally induced alterations in SPA or to assess natural variations in SPA that might confound data interpretation.

Use of Body-Mounted Cameras to Enhance Data Collection: An Evaluation of Two Arthropod Sampling Techniques

A study was conducted that compared the effectiveness of a sweepnet versus a vacuum suction device for collecting arthropods in cotton. The study differs from previous research in that body-mounted action cameras (B-MACs) were used to record the activity of the person conducting the arthropod collections. The videos produced by the B-MACs were then analyzed with behavioral event recording software to quantify various aspects of the sampling process. The sampler's speed and the number of sampling sweeps or vacuum suctions taken over a fixed distance (12.2 m) of cotton were two of the more significant sampling characteristics quantified for each method. The arthropod counts obtained, combined with the analyses of the videos, enabled us to estimate arthropod sampling efficiency for each technique based on fixed distance, time, and sample unit measurements. Data revealed that the vacuuming was the most precise method for collecting arthropods in the relatively small cotton research plots. However, data also indicates that the sweepnet method would be more efficient for collecting most of the cotton-dwelling arthropod taxa, especially if the sampler could continuously sweep for at least 1 min or ≥80 m (e.g., in larger research plots). The B-MACs are inexpensive and non-cumbersome, the video images generated are outstanding, and they can be archived to provide permanent documentation of a research project. The methods described here could be useful for other types of field-based research to enhance data collection efficiency.

In vitro investigations of propulsion during laser lithotripsy using video tracking

OBJECTIVES

Ureteroscopic laser lithotripsy is an important and widely used method for destroying ureter stones. It represents an alternative to ultrasonic and pneumatic lithotripsy techniques. Although these techniques have been thoroughly investigated, the influence of some physical parameters that may be relevant to further improve the treatment results is not fully understood. One crucial topic is the propulsive stone movement induced by the applied laser pulses. To simplify and speed up the optimization of laser parameters in this regard, a video tracking method was developed in connection with a vertical column setup that allows recording and subsequently analyzing the propulsive stone movement in dependence of different laser parameters in a particularly convenient and fast manner.

MATERIALS AND METHODS

Pulsed laser light was applied from below to a cubic BegoStone phantom loosely guided within a vertical column setup. The video tracking method uses an algorithm to determine the vertical stone position in each frame of the recorded scene. The time-dependence of the vertical stone position is characterized by an irregular series of peaks. By analyzing the slopes of the peaks in this signal it was possible to determine the mean upward stone velocity for a whole pulse train and to compare it for different laser settings. For a proof of principle of the video tracking method, a specific pulse energy setting (1 J/pulse) was used in combination with three different pulse durations: short pulse (0.3 ms), medium pulse (0.6 ms), and long pulse (1.0 ms). The three pulse durations were compared in terms of their influence on the propulsive stone movement in terms of upward velocity. Furthermore, the propulsions induced by two different pulse energy settings (0.8 J/pulse and 1.2 J/pulse) for a fixed pulse duration (0.3 ms) were compared. A pulse repetition rate of 10 Hz was chosen for all experiments, and for each laser setting, the experiment was repeated on 15 different freshly prepared stones. The latter set of experiments was compared with the results of previous propulsion measurements performed with a pendulum setup.

RESULTS

For a fixed pulse energy (1 J/pulse), the mean upward propulsion velocity increased (from 120.0 to 154.9 mm · s^-1 ) with decreasing pulse duration. For fixed pulse duration (0.3 ms), the mean upward propulsion velocity increased (from 91.9 to 123.3 mm · s^-1 ) with increasing pulse energy (0.8 J/pulse and 1.2 J/pulse). The latter result corresponds roughly to the one obtained with the pendulum setup (increase from 61 to 105 mm · s^-1 ). While the mean propulsion velocities for the two different pulse energies were found to differ significantly (P < 0.001) for the two experimental and analysis methods, the standard deviations of the measured mean propulsion velocities were considerably smaller in case of the vertical column method with video tracking (12% and 15% for n = 15 freshly prepared stones) than in case of the pendulum method (26% and 41% for n = 50 freshly prepared stones), in spite of the considerably smaller number of experiment repetitions ("sample size") in the first case.

CONCLUSION

The proposed vertical column method with video tracking appears advantageous compared to the pendulum method in terms of the statistical significance of the obtained results. This may partly be understood by the fact that the entire motion of the stones contributes to the data analysis, rather than just their maximum distance from the initial position. The key difference is, however, that the pendulum method involves only one single laser pulse in each experiment run, which renders this method rather tedious to perform. Furthermore, the video tracking method appears much better suited to model a clinical lithotripsy intervention that utilizes longer series of laser pulses at higher repetition rates. The proposed video tracking method can conveniently and quickly deliver results for a large number of laser pulses that can easily be averaged. An optimization of laser settings to achieve minimal propulsive stone movement should thus be more easily feasible with the video tracking method in connection with the vertical column setup. Lasers Surg. Med. 50:333-339, 2018. © 2017 Wiley Periodicals, Inc.

Vision System for Coarsely Estimating Motion Parameters for Unknown Fast Moving Objects in Space

Motivated by biological interests in analyzing navigation behaviors of flying animals, we attempt to build a system measuring their motion states. To do this, in this paper, we build a vision system to detect unknown fast moving objects within a given space, calculating their motion parameters represented by positions and poses. We proposed a novel method to detect reliable interest points from images of moving objects, which can be hardly detected by general purpose interest point detectors. 3D points reconstructed using these interest points are then grouped and maintained for detected objects, according to a careful schedule, considering appearance and perspective changes. In the estimation step, a method is introduced to adapt the robust estimation procedure used for dense point set to the case for sparse set, reducing the potential risk of greatly biased estimation. Experiments are conducted against real scenes, showing the capability of the system of detecting multiple unknown moving objects and estimating their positions and poses.

A Study of Relationships among Technical, Tactical, Physical Parameters and Final Outcomes in Elite Soccer Matches as Analyzed by a Semiautomatic Video Tracking System

The performance of a soccer team depends on many factors such as decision-making, cognitive and physical skills, and dynamic ever-changing space-time interactions between teammate and opponents in relation to the ball. Seventy ( n = 70) matches of the Italian SERIE A season 2013-2014 were investigated to analyze the mean performance of 360 players in terms of physical (physical efficiency index; PEI) and technical-tactical (technical efficiency index; TEI) standpoints. Using a semiautomatic video analysis system that has incorporated new parameters able to measure technical-tactical and physical efficiency (Patent IB2010/002593, 2011-ISA), the correlation between these new variables and how much it relates to the likelihood of winning were verified. Correlations between TEI and PEI were significant ( n = 140, r = .60, p < .001), and TEI showed a higher likelihood of winning than PEI factors ( p < .0001 vs. .0001, CI 95% [1.64, 3.00] vs. [1.28, 2.07]). Higher TEI and TEI + PEI differences between the teams were associated with a greater likelihood of winning, but PEI differences were not. Key performance indicators and this performance assessment method might be useful to better understand what determines winning and to assist the overall training process and match management.

Non-Instrumental Movement Inhibition (NIMI) Differentially Suppresses Head and Thigh Movements during Screenic Engagement: Dependence on Interaction

BACKGROUND

Estimating engagement levels from postural micromovements has been summarized by some researchers as: increased proximity to the screen is a marker for engagement, while increased postural movement is a signal for disengagement or negative affect. However, these findings are inconclusive: the movement hypothesis challenges other findings of dyadic interaction in humans, and experiments on the positional hypothesis diverge from it.

HYPOTHESES

(1) Under controlled conditions, adding a relevant visual stimulus to an auditory stimulus will preferentially result in Non-Instrumental Movement Inhibition (NIMI) of the head. (2) When instrumental movements are eliminated and computer-interaction rate is held constant, for two identically-structured stimuli, cognitive engagement (i.e., interest) will result in measurable NIMI of the body generally.

METHODS

Twenty-seven healthy participants were seated in front of a computer monitor and speakers. Discrete 3-min stimuli were presented with interactions mediated via a handheld trackball without any keyboard, to minimize instrumental movements of the participant's body. Music videos and audio-only music were used to test hypothesis (1). Time-sensitive, highly interactive stimuli were used to test hypothesis (2). Subjective responses were assessed via visual analog scales. The computer users' movements were quantified using video motion tracking from the lateral aspect. Repeated measures ANOVAs with Tukey post hoc comparisons were performed.

RESULTS

For two equivalently-engaging music videos, eliminating the visual content elicited significantly increased non-instrumental movements of the head (while also decreasing subjective engagement); a highly engaging user-selected piece of favorite music led to further increased non-instrumental movement. For two comparable reading tasks, the more engaging reading significantly inhibited (42%) movement of the head and thigh; however, when a highly engaging video game was compared to the boring reading, even though the reading task and the game had similar levels of interaction (trackball clicks), only thigh movement was significantly inhibited, not head movement.

CONCLUSIONS

NIMI can be elicited by adding a relevant visual accompaniment to an audio-only stimulus or by making a stimulus cognitively engaging. However, these results presume that all other factors are held constant, because total movement rates can be affected by cognitive engagement, instrumental movements, visual requirements, and the time-sensitivity of the stimulus.

VideoHacking: Automated Tracking and Quantification of Locomotor Behavior with Open Source Software and Off-the-Shelf Video Equipment

Differences in nervous system function can result in differences in behavioral output. Measurements of animal locomotion enable the quantification of these differences. Automated tracking of animal movement is less labor-intensive and bias-prone than direct observation, and allows for simultaneous analysis of multiple animals, high spatial and temporal resolution, and data collection over extended periods of time. Here, we present a new video-tracking system built on Python-based software that is free, open source, and cross-platform, and that can analyze video input from widely available video capture devices such as smartphone cameras and webcams. We validated this software through four tests on a variety of animal species, including larval and adult zebrafish (Danio rerio), Siberian dwarf hamsters (Phodopus sungorus), and wild birds. These tests highlight the capacity of our software for long-term data acquisition, parallel analysis of multiple animals, and application to animal species of different sizes and movement patterns. We applied the software to an analysis of the effects of ethanol on thigmotaxis (wall-hugging) behavior on adult zebrafish, and found that acute ethanol treatment decreased thigmotaxis behaviors without affecting overall amounts of motion. The open source nature of our software enables flexibility, customization, and scalability in behavioral analyses. Moreover, our system presents a free alternative to commercial video-tracking systems and is thus broadly applicable to a wide variety of educational settings and research programs.

Bonsai: an event-based framework for processing and controlling data streams

The design of modern scientific experiments requires the control and monitoring of many different data streams. However, the serial execution of programming instructions in a computer makes it a challenge to develop software that can deal with the asynchronous, parallel nature of scientific data. Here we present Bonsai, a modular, high-performance, open-source visual programming framework for the acquisition and online processing of data streams. We describe Bonsai's core principles and architecture and demonstrate how it allows for the rapid and flexible prototyping of integrated experimental designs in neuroscience. We specifically highlight some applications that require the combination of many different hardware and software components, including video tracking of behavior, electrophysiology and closed-loop control of stimulation.

Sexually dimorphic activation of dopaminergic areas depends on affiliation during courtship and pair formation

For many species, dyadic interaction during courtship and pair bonding engage intense emotional states that control approach or avoidance behavior. Previous studies have shown that one component of a common social brain network (SBN), dopaminergic areas, are highly engaged during male songbird courtship of females. We tested whether the level of activity in dopaminergic systems of both females and males during courtship is related to their level of affiliation. In order to objectively quantify affiliative behaviors, we developed a system for tracking the position of both birds during free interaction sessions. During a third successive daily interaction session, there was a range of levels of affiliation among bird pairs, as quantified by several position and movement parameters. Because both positive and negative social interactions were present, we chose to characterize affiliation strength by pair valence. As a potential neural system involved in regulating pair valence, the level of activity of the dopaminergic group A11 (within the central gray) was selectively reduced in females of positive valence pairs. Further, activation of non-dopaminergic neurons in VTA was negatively related to valence, with this relationship strongest in ventral VTA of females. Together, these results suggest that inhibition of fear or avoidance networks may be associated with development of close affiliation, and highlight the importance of negative as well as positive emotional states in the process of courtship, and in development of long-lasting social bonds.

The repellency of lemongrass oil against stable flies, tested using video tracking

Lemongrass oil (Cymbopogon citratus) is an effective repellent against mosquitoes (Diptera: Culicidae) and house flies (Diptera: Muscidae). In this study, its effectiveness was assessed on stable flies (Diptera: Muscidae) in laboratory conditions. First, we demonstrated that lemongrass oil is an active substance for antennal olfactory receptor cells of Stomoxys calcitrans as indicated by a significant increase in the electroantennogram responses to increasing doses of lemongrass oil. Feeding-choice tests in a flight cage with stable flies having access to two blood-soaked sanitary pads, one of which was treated with lemongrass oil, showed that stable flies (n = 24) spent significantly more time in the untreated zone (median value = 218.4 s) than in the treated zone (median value = 63.7 s). No stable flies fed on the treated pad, whereas nine fed on the untreated pad. These results suggest that lemongrass oil could be used as an effective repellent against stable flies. Additional studies to confirm its spatial repellent and feeding deterrent effects are warranted.

A real-time algorithm for the approximation of level-set-based curve evolution

In this paper, we present a complete and practical algorithm for the approximation of level-set-based curve evolution suitable for real-time implementation. In particular, we propose a two-cycle algorithm to approximate level-set-based curve evolution without the need of solving partial differential equations (PDEs). Our algorithm is applicable to a broad class of evolution speeds that can be viewed as composed of a data-dependent term and a curve smoothness regularization term. We achieve curve evolution corresponding to such evolution speeds by separating the evolution process into two different cycles: one cycle for the data-dependent term and a second cycle for the smoothness regularization. The smoothing term is derived from a Gaussian filtering process. In both cycles, the evolution is realized through a simple element switching mechanism between two linked lists, that implicitly represents the curve using an integer valued level-set function. By careful construction, all the key evolution steps require only integer operations. A consequence is that we obtain significant computation speedups compared to exact PDE-based approaches while obtaining excellent agreement with these methods for problems of practical engineering interest. In particular, the resulting algorithm is fast enough for use in real-time video processing applications, which we demonstrate through several image segmentation and video tracking experiments.